Skip to main content
SpringerLink
Log in

Brain-Computer Interface for Smart Vehicle: Detection of Braking Intention During Simulated Driving

  • Chapter
Recent Progress in Brain and Cognitive Engineering

Part of the book series: Trends in Augmentation of Human Performance ((TAHP,volume 5))

  • 1603 Accesses

Abstract

It is most essential to stop a vehicle in time for assuring a driver’s safety. In this study, a simulated driving environment was implemented to study the neural correlation of braking intention in diverse driving situations. We further investigated to what extent these neural correlates can be used to detect a participant’s braking intention prior to the behavioral response. A feature combination method was proposed for the enhancement of detection performance and additional classification of emergency braking triggered by stimuli and voluntary braking. It consists of event-related potential (ERP), readiness potential (RP), and event-related desynchronization (ERD) features. Fifteen participants drove a virtual vehicle and were exposed to the diversified traffic situations in the constructed simulator framework, while technical signals (i.e., gas pedal and brake pedal), electroencephalogram (EEG) and electromyogram (EMG) signals were measured. After that, the neural correlation of the measured signals was analyzed. The proposed framework shows excellent detection performance for various kinds of driver’s braking intention. Our study suggests that a driver’s braking intention is characterized by specific neural patterns of sensory perception and processing, as well as motor preparation and execution, which can be utilized by smart vehicle technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The parts have been taken verbatim from the author’s prior publication [39] marked with bold in Introduction and Discussion Section.

References

  1. Schmidt EA, Schrauf M, Simon M, Fritzsche M, Buchner A, Kincses WE (2009) Driver’s misjudgement of vigilance state during prolonged monotonous daytime driving. Accid Anal Prev 41(5):1087–1093

    Article  PubMed  Google Scholar 

  2. Papadelis C, Chen Z, Kourtidou-Papadeli C, Bamidis PD, Chouvarda I, Bekiaris E, Maglaveras N (2007) Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents. Clin Neurophysiol 118(9):1906–1922

    Article  PubMed  Google Scholar 

  3. Kohlmorgen J, Dornhege G, Braun M, Blankertz B, Müller KR, Curio G, Hagemann K, Bruns A, Schrauf M, Kincses W (2007) Improving human performance in a real operating environment through real-time mental workload detection. In: Toward brain-computer interfacing. MIT Press, Cambridge, MA, pp 409–422

    Google Scholar 

  4. Schmidt WE, Kincses WE, Schrauf M, Haufe S, Schubert R, Curio G (2007) Assessing driver’s vigilance state during monotonous driving. In: Proceedings of the 4th International symposium on human factors in driving assessment, training, and vehicle design, Washington, USA, pp 138–145

    Google Scholar 

  5. Hood D, Joseph D, Rakotonirainy A, Sridhara S, Fookes C (2012) Use of brain computer interface to drive: preliminary results. In: Proceedings of the 4th International conference on automotive user interfaces and interactive vehicular applications 2012, Eindhoven, Netherlands, pp 103–106

    Google Scholar 

  6. Khaliliardali Z, Chavarriaga R, Gheorghe LA, Millán JdR (2012) Detection of anticipatory brain potentials during car driving. In: Proceedings of the IEEE conference on EMBS 2012, San Diego, USA, pp 3829–3832

    Google Scholar 

  7. Luzheng B, Nini L, Xin-an F (2012) A brain-computer interface in the context of a head up display system. In: Proceedings of the ICME International conference on complex medical engineering 2012, Kobe, Japan, pp 241–244

    Google Scholar 

  8. Müller KR, Tangermann M, Dornhege G, Krauledat M, Curio G, Blankertz B (2008) Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring. J Neurosci Methods 167(1):82–90

    Article  PubMed  Google Scholar 

  9. Renold H, Chavarriaga R, Gheorghe LA, Millán JdR (2014) EEG correlates of active visual search during simulated driving: an exploratory study. In: Proceedings of the IEEE International conference on systems, man and cybernetics, San Diego, CA, USA, 5–8 Oct 2014, pp 2815–2820

    Google Scholar 

  10. Zhang H, Chavarriaga R, Gheorghe LA, Millán JdR (2013) Inferring driver’s turning direction through detection of error related brain activity. In: 35th Annual international conference of the IEEE engineering in medicine and biology society, Osaka, Japan, 3–7 July 2013, pp 2196–2199

    Google Scholar 

  11. Gheorghe LA, Chavarriaga R, Millán JdR (2013) Steering timing prediction in a driving simulator task. In: 35th Annual international conference of the IEEE engineering in medicine and biology society, Osaka, Japan, 3–7 July 2013, pp 6913–6916

    Google Scholar 

  12. Haufe S, Treder MS, Gugler MF, Sagebaum M, Curio G, Blankertz B (2011) EEG potentials predict upcoming emergency brakings during simulated driving. J Neural Eng 8(5):056001

    Article  PubMed  Google Scholar 

  13. Shibasaki H, Hallett M (2006) What is the bereitschaftspotential? Clin Neurophysiol 117(11):2341–2356

    Article  PubMed  Google Scholar 

  14. Sharbrough F, Chartrian GE, Lesser RP, Lüders H, Nuwer M, Picton TW (1991) American electroencephalographic society guidelines for standard electrode position nomenclature. J Clin Neurophysiol 8(2):200–202

    Article  Google Scholar 

  15. Blankertz B, Tomioka R, Lemm S, Kawanabe M, Müller KR (2008) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 25(1):41–56

    Article  Google Scholar 

  16. Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874

    Article  Google Scholar 

  17. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    Article  CAS  PubMed  Google Scholar 

  18. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Hum Genet 7(2):179–188

    Google Scholar 

  19. Duda RO, Hard PE, Stork DG (2000) Pattern classification. Wiley, New York

    Google Scholar 

  20. Lemm S, Blankertz B, Dickhaus T, Müller KR (2011) Introduction to machine learning for brain imaging. NeuroImage 56(2):387–399

    Article  PubMed  Google Scholar 

  21. Tomioka R, Müller KR (2010) A regularized discriminative framework for EEG analysis with application to brain-computer interface. NeuroImage 49(1):415–432

    Article  PubMed  Google Scholar 

  22. Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84(405):165–175

    Article  Google Scholar 

  23. Blankertz B, Lemm S, Treder M, Haufe S, Müller KR (2011) Single-trial analysis and classification of ERP components – a tutorial. NeuroImage 56(2):814–825

    Article  PubMed  Google Scholar 

  24. Ledoit O, Wolf M (2004) A well-conditioned estimator for large-dimensional covariance matrices. J Multivar Anal 88(2):365–411

    Article  Google Scholar 

  25. Schäfer J, Strimmer K (2005) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol 4(1):32

    Google Scholar 

  26. Gibbons JD, Chakraborti S (2011) Nonparametric statistical inference. Marcel Dekker, New York

    Book  Google Scholar 

  27. Bonferroni CE (1936) Teoria Statistica delle Classi e Calcolo delle Probabilitá. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 8:3–62

    Google Scholar 

  28. Sutton S, Braren M, Zubin J, John ER (1965) Evoked-potential correlates of stimulus uncertainty. Science 150(3700):1187–1188

    Article  CAS  PubMed  Google Scholar 

  29. Kim I-H, Kim J-W, Haufe S, Lee S-W (2013) Detection of multi-class emergency situations during simulated driving from ERP. In: 2013 IEEE International winter workshop on brain-computer interface, Jeongseon, Korea, pp 49–51

    Google Scholar 

  30. Kornhuber HH, Deecke L (1965) Hirnpotentialänderungen bei Willkürbeweguugen und passive Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale. Pügers Arch 284:1–17

    Article  CAS  Google Scholar 

  31. Penfiled W, Boldrey E (1937) Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain J Neurol 60:389–443

    Article  Google Scholar 

  32. Libet B, Gleason CA, Wright EW, Pearl DK (1983) Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential): the unconscious initiation of a freely voluntary act. Brain J Neurol 106(3):623–642

    Article  Google Scholar 

  33. Haggard P, Eimer M (1999) On the relation between brain potentials and the awareness of voluntary movements. Exp Brain Res 126(1):128–133

    Article  CAS  PubMed  Google Scholar 

  34. Dornhege G, Blankertz B, Curio G, Müller KR (2004) Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Trans Bio-Med Eng 51(6):993–1002

    Article  Google Scholar 

  35. Blankertz B, Dornhege G, Schäfer C, Krepki R, Kohlmorgen J, Müller KR, Kunzmann V, Losch F, Curio G (2003) Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis. IEEE Trans Neural Syst Rehabil Eng 11(2):127–131

    Article  PubMed  Google Scholar 

  36. Pfurtscheller G, Lopes da Silva FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110(11):1842–1857

    Article  CAS  PubMed  Google Scholar 

  37. Leocani L, Toro C, Zhuang P, Gerloff C, Hallett M (2001) Event-related desynchronization in reaction time paradigms: a comparison with event-related potentials and corticospinal excitability. Clin Neurophysiol 112(5):923–930

    Article  CAS  PubMed  Google Scholar 

  38. Haufe S, Kim J-W, Kim I-H, Sonnleitner A, Schrauf M, Curio G, Blankertz B (2014) Electrophysiology-based detection of emergency braking intention in real-world driving. J Neural Eng 11(5):056011

    Article  PubMed  Google Scholar 

  39. Kim I-H, Kim J-W, Haufe S, Lee S-W (2015) Detection of braking intention in diverse situations during simulated driving based on EEG feature combination. J Neural Eng 12(1):016001

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2015R1A2A1A05001867). The authors acknowledge the use of text from the own prior publication [39] in this article. Jeong-Woo Kim and Seong-Whan Lee thank their co-authors for allowing them to use materials from prior joint publication [39].

Author information

Author notes

    Authors and Affiliations

    1. Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

      Jeong-Woo Kim, Il-Hwa Kim & Seong-Whan Lee

    2. Neural Engineering Group, Department of Biomedical Engineering, The City College of New York, 140 Street & Convent Avenue, New York, NY, 10031, USA

      Stefan Haufe

    3. Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Marchstr. 23, 10587, Berlin, Germany

      Stefan Haufe

    4. Bernstein Focus Neurotechnology, Berlin, Germany

      Stefan Haufe

    Authors
    1. Jeong-Woo Kim
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Il-Hwa Kim
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Stefan Haufe
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Seong-Whan Lee
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Seong-Whan Lee .

    Editor information

    Editors and Affiliations

    1. Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea (Republic of)

      Seong-Whan Lee

    2. Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Tübingen, Germany

      Heinrich H. Bülthoff

    3. Machine Learning Group, Berlin Institute of Technology, Berlin, Germany

      Klaus-Robert Müller

    Rights and permissions

    Reprints and permissions

    Copyright information

    © 2015 Springer Science+Business Media Dordrecht

    About this chapter

    Cite this chapter

    Kim, JW., Kim, IH., Haufe, S., Lee, SW. (2015). Brain-Computer Interface for Smart Vehicle: Detection of Braking Intention During Simulated Driving. In: Lee, SW., Bülthoff, H., Müller, KR. (eds) Recent Progress in Brain and Cognitive Engineering. Trends in Augmentation of Human Performance, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7239-6_2

    Download citation

    Publish with us

    Policies and ethics

    Search

    Navigation